Here is my code:

Reformatting/Cleaning Up Data

Raw Counts File

cnts <- read.csv('raw_counts_Cooper_data_final.csv')

rownames(cnts) <- cnts$X

cnts$X <- NULL

cnts <- as.matrix(cnts)

This takes raw_counts file and corrects the columns and index of the dataframe, then converts the file from a data frame to a matrix.

Meta Data

column_data <- read.csv('Cooper_Data_Meta.csv')

rownames(column_data) <- column_data$Sample

column_data <- column_data[,c("Tissue","Treatment","Stage")]


#column_data$Tissue <- factor(column_data$Tissue)
#column_data$Treatment <- factor(column_data$Treatment)
#column_data$Stage <- factor(column_data$Stage)

#column_data$Treatment <- NULL
#column_data$Stage <- NULL
#column_data$Sample <- NULL

column_data
##                    Tissue Treatment         Stage
## SRR10912052    Serous_EOC   Control            4B
## SRR10912053    Serous_EOC  Platinum            4B
## SRR10912054    Serous_EOC   Control             4
## SRR10912055    Serous_EOC  Platinum             4
## SRR10912056    Serous_EOC   Control            3C
## SRR10912057    Serous_EOC  Platinum            3C
## SRR10912058    Serous_EOC   Control            3C
## SRR10912059    Serous_EOC  Platinum            3C
## SRR10912060    Serous_EOC   Control            3C
## SRR10912061    Serous_EOC  Platinum            3C
## SRR10912062    Serous_EOC   Control             4
## SRR10912063    Serous_EOC  Platinum             4
## SRR10912064    Serous_EOC   Control            3C
## SRR10912065    Serous_EOC  Platinum            3C
## SRR10912066    Serous_EOC   Control            3C
## SRR10912067    Serous_EOC  Platinum            3C
## SRR10912068    Serous_EOC   Control            3C
## SRR10912069    Serous_EOC  Platinum            3C
## SRR10912070    Serous_EOC   Control             4
## SRR10912071    Serous_EOC  Platinum             4
## SRR10912072    Serous_EOC   Control            3C
## SRR10912073    Serous_EOC   Control            3C
## SRR10912074    Serous_EOC  Platinum            3C
## SRR10912075    Serous_EOC   Control            3C
## SRR10912076    Serous_EOC  Platinum            3C
## SRR10912077    Serous_EOC  Platinum            3C
## SRR10912078    Serous_EOC   Control            3C
## SRR10912079    Serous_EOC  Platinum            3C
## SRR10912080    Serous_EOC   Control            3C
## SRR10912081    Serous_EOC  Platinum            3C
## SRR10912082    Serous_EOC   Control             4
## SRR10912083    Serous_EOC  Platinum             4
## SRR10912084    Serous_EOC   Control            3C
## SRR10912085    Serous_EOC  Platinum            3C
## SRR10912086    Serous_EOC   Control            3C
## SRR10912087    Serous_EOC  Platinum            3C
## SRR10912088    Serous_EOC   Control            3C
## SRR10912089    Serous_EOC   Control            3C
## SRR10912090    Serous_EOC   Control            3C
## SRR10912091    Serous_EOC   Control            3C
## SRR10912092    Serous_EOC   Control            3C
## SRR10912093    Serous_EOC   Control             4
## SRR10912094    Serous_EOC   Control            3C
## SRR10912095    Serous_EOC   Control            3C
## SRR10912096    Serous_EOC   Control            3C
## SRR10912097    Serous_EOC   Control            3C
## SRR10912098    Serous_EOC   Control            3C
## SRR10912099    Serous_EOC   Control            3C
## SRR10912100    Serous_EOC   Control            3C
## SRR10912101    Serous_EOC   Control            3C
## SRR10912102    Serous_EOC   Control            3C
## SRR10912103    Serous_EOC   Control            2C
## SRR10912104    Serous_EOC   Control            3C
## SRR10912105    Serous_EOC   Control             4
## SRR10912106    Serous_EOC   Control            3C
## SRR10912107    Serous_EOC   Control            3C
## SRR10912108    Serous_EOC   Control            3C
## SRR10912109    Serous_EOC   Control             4
## SRR10912110    Serous_EOC   Control            3C
## SRR10912111    Serous_EOC   Control            3C
## SRR10912112    Serous_EOC   Control            3C
## SRR10912113    Serous_EOC   Control            3C
## SRR10912114    Serous_EOC   Control            3C
## SRR10912115    Serous_EOC   Control             4
## SRR10912116    Serous_EOC   Control            3C
## SRR10912117    Serous_EOC   Control            3C
## SRR10912118    Serous_EOC   Control            3C
## SRR10912119    Serous_EOC   Control            3C
## SRR10912120    Serous_EOC   Control             4
## SRR10912121    Serous_EOC   Control            3C
## SRR10912122    Serous_EOC   Control            3C
## SRR10912123    Serous_EOC   Control            3C
## SRR10912124    Serous_EOC   Control             4
## SRR10912125    Serous_EOC   Control            3C
## SRR10912126    Serous_EOC   Control             4
## SRR10912127 Benign_Tissue   Control patientid: 61
## SRR10912128 Benign_Tissue   Control patientid: 62
## SRR10912129 Benign_Tissue   Control patientid: 63
## SRR10912130 Benign_Tissue   Control patientid: 64
## SRR10912131 Benign_Tissue   Control patientid: 65
## SRR10912132 Benign_Tissue   Control patientid: 66
## SRR10912133 Benign_Tissue   Control patientid: 67
## SRR10912134 Benign_Tissue   Control patientid: 68
## SRR10912135 Benign_Tissue   Control patientid: 69
## SRR10912136 Benign_Tissue   Control patientid: 70
## SRR10912137 Benign_Tissue   Control patientid: 71
## SRR10912138       Ascites   Control            4A
## SRR10912139       Ascites   Control            3C
## SRR10912140       Ascites   Control            3C
## SRR10912141       Ascites   Control            3C
## SRR10912142    Serous_EOC   Control            4A
## SRR10912143    Serous_EOC   Control            3C
## SRR10912144 Benign_Tissue   Control patientid: 78
## SRR10912145    Serous_EOC   Control            3C
## SRR10912146    Serous_EOC   Control            3C
## SRR10912147       Ascites   Control            3C
## SRR10912148       Ascites   Control            3C
## SRR10912149       Ascites   Control            3C
## SRR10912150       Ascites   Control            3C
## SRR10912151       Ascites   Control            3C
## SRR10912152       Ascites   Control             4
## SRR10912153       Ascites   Control            3C
## SRR10912154       Ascites   Control            3C
## SRR10912155       Ascites   Control            3C
## SRR10912156       Ascites   Control            3C
## SRR10912157       Ascites   Control            3C
## SRR10912158       Ascites   Control            3C
## SRR10912159       Ascites   Control            3C
## SRR10912160       Ascites   Control            3C
## SRR10912161       Ascites   Control            2C
## SRR10912162       Ascites   Control            3C
## SRR10912163       Ascites   Control             4
## SRR10912164       Ascites   Control            3C
## SRR10912165       Ascites   Control            3C
## SRR10912166       Ascites   Control            3C
## SRR10912167       Ascites   Control            3C
## SRR10912168       Ascites   Control             4
## SRR10912169       Ascites   Control            3C
## SRR10912170       Ascites   Control            3C
## SRR10912171       Ascites   Control            3C
## SRR10912172       Ascites   Control             4

This takes the metadata, or column data, file and corrects the columns and index of the dataframe, then converts the file from a data frame to a matrix.

Check if rownames and columnnames Match

all(rownames(column_data) %in% colnames(cnts))
## [1] TRUE
all(rownames(column_data) == colnames(cnts))
## [1] TRUE

This checks if the rownames and column names match.

DeSeq2

library("DESeq2")
## Loading required package: S4Vectors
## Loading required package: stats4
## Loading required package: BiocGenerics
## 
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
## 
##     anyDuplicated, aperm, append, as.data.frame, basename, cbind,
##     colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
##     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
##     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
##     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which.max, which.min
## 
## Attaching package: 'S4Vectors'
## The following object is masked from 'package:utils':
## 
##     findMatches
## The following objects are masked from 'package:base':
## 
##     expand.grid, I, unname
## Loading required package: IRanges
## Loading required package: GenomicRanges
## Loading required package: GenomeInfoDb
## Loading required package: SummarizedExperiment
## Loading required package: MatrixGenerics
## Loading required package: matrixStats
## 
## Attaching package: 'MatrixGenerics'
## The following objects are masked from 'package:matrixStats':
## 
##     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
##     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
##     colWeightedMeans, colWeightedMedians, colWeightedSds,
##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
##     rowWeightedSds, rowWeightedVars
## Loading required package: Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
## 
## Attaching package: 'Biobase'
## The following object is masked from 'package:MatrixGenerics':
## 
##     rowMedians
## The following objects are masked from 'package:matrixStats':
## 
##     anyMissing, rowMedians
dds <- DESeqDataSetFromMatrix(countData = cnts,
                              colData = column_data,
                              design = ~ Tissue)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
dds
## class: DESeqDataSet 
## dim: 20643 121 
## metadata(1): version
## assays(1): counts
## rownames(20643): ENSG00000000003 ENSG00000000005 ... ENSG00000273439
##   ENSG00000273452
## rowData names(0):
## colnames(121): SRR10912052 SRR10912053 ... SRR10912171 SRR10912172
## colData names(3): Tissue Treatment Stage

Pre-filtering

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

Prefiltering can improve visualization as features with no information are not plotted.

Factor Levels

dds$Tissue <- relevel(dds$Tissue, ref = "Benign_Tissue")

“By default, R will choose a reference level for factors based on alphabetical order. Then, if you never tell the DESeq2 functions which level you want to compare against (e.g. which level represents the control group), the comparisons will be based on the alphabetical order of the levels. There are two solutions: you can either explicitly tell results which comparison to make using the contrast argument (this will be shown later), or you can explicitly set the factors levels. In order to see the change of reference levels reflected in the results names, you need to either run DESeq or nbinomWaldTest/nbinomLRT after the re-leveling operation.”

Setting Benign Tissue as the reference level.

Differential expression analysis

dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 1168 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
resultsNames(dds)
## [1] "Intercept"                          "Tissue_Ascites_vs_Benign_Tissue"   
## [3] "Tissue_Serous_EOC_vs_Benign_Tissue"
#summary(res)
res_Serous_EOC <- results(dds, name="Tissue_Serous_EOC_vs_Benign_Tissue")

res_Serous_EOC
## log2 fold change (MLE): Tissue Serous EOC vs Benign Tissue 
## Wald test p-value: Tissue Serous EOC vs Benign Tissue 
## DataFrame with 19975 rows and 6 columns
##                   baseMean log2FoldChange     lfcSE      stat      pvalue
##                  <numeric>      <numeric> <numeric> <numeric>   <numeric>
## ENSG00000000003  1359.4263      -0.790170  0.248072 -3.185252 1.44628e-03
## ENSG00000000005    28.6264      -0.512972  0.913941 -0.561275 5.74610e-01
## ENSG00000000419   945.1298       0.408815  0.156660  2.609561 9.06584e-03
## ENSG00000000457   339.0323      -0.139602  0.124784 -1.118752 2.63246e-01
## ENSG00000000460   189.3951       1.346737  0.231249  5.823762 5.75375e-09
## ...                    ...            ...       ...       ...         ...
## ENSG00000273294 18.6100019       0.115738  0.774778 0.1493822    0.881252
## ENSG00000273331  1.6135503       2.457218  1.142924 2.1499398    0.031560
## ENSG00000273398  5.7510137       0.562653  0.536606 1.0485403    0.294390
## ENSG00000273439 22.4666648       0.669630  0.415259 1.6125583    0.106841
## ENSG00000273452  0.0657423       0.197680  4.954781 0.0398967    0.968175
##                        padj
##                   <numeric>
## ENSG00000000003 3.91623e-03
## ENSG00000000005 6.64751e-01
## ENSG00000000419 1.99003e-02
## ENSG00000000457 3.58693e-01
## ENSG00000000460 5.14674e-08
## ...                     ...
## ENSG00000273294   0.9141591
## ENSG00000273331   0.0593248
## ENSG00000273398   0.3925197
## ENSG00000273439   0.1682453
## ENSG00000273452          NA
resLFC_Serous_EOC <- lfcShrink(dds, coef="Tissue_Serous_EOC_vs_Benign_Tissue", type="apeglm")
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
##     sequence count data: removing the noise and preserving large differences.
##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
resLFC_Serous_EOC
## log2 fold change (MAP): Tissue Serous EOC vs Benign Tissue 
## Wald test p-value: Tissue Serous EOC vs Benign Tissue 
## DataFrame with 19975 rows and 5 columns
##                   baseMean log2FoldChange     lfcSE      pvalue        padj
##                  <numeric>      <numeric> <numeric>   <numeric>   <numeric>
## ENSG00000000003  1359.4263      -0.707614  0.238990 1.44628e-03 3.91623e-03
## ENSG00000000005    28.6264      -0.256894  0.633896 5.74610e-01 6.64751e-01
## ENSG00000000419   945.1298       0.397866  0.155273 9.06584e-03 1.99003e-02
## ENSG00000000457   339.0323      -0.136250  0.123498 2.63246e-01 3.58693e-01
## ENSG00000000460   189.3951       1.301129  0.234105 5.75375e-09 5.14674e-08
## ...                    ...            ...       ...         ...         ...
## ENSG00000273294 18.6100019      0.0682127  0.587723    0.881252   0.9141591
## ENSG00000273331  1.6135503      3.6905904  1.538611    0.031560   0.0593248
## ENSG00000273398  5.7510137      0.4212642  0.487248    0.294390   0.3925197
## ENSG00000273439 22.4666648      0.5644270  0.401099    0.106841   0.1682453
## ENSG00000273452  0.0657423      0.0337805  0.882672    0.968175          NA
res_Ascites <- results(dds, name="Tissue_Ascites_vs_Benign_Tissue")

res_Ascites
## log2 fold change (MLE): Tissue Ascites vs Benign Tissue 
## Wald test p-value: Tissue Ascites vs Benign Tissue 
## DataFrame with 19975 rows and 6 columns
##                   baseMean log2FoldChange     lfcSE      stat      pvalue
##                  <numeric>      <numeric> <numeric> <numeric>   <numeric>
## ENSG00000000003  1359.4263      -0.914937  0.275916  -3.31600 9.13167e-04
## ENSG00000000005    28.6264      -4.565551  1.029674  -4.43398 9.25110e-06
## ENSG00000000419   945.1298       0.487980  0.175152   2.78604 5.33562e-03
## ENSG00000000457   339.0323      -0.448853  0.141450  -3.17321 1.50761e-03
## ENSG00000000460   189.3951       1.099443  0.259719   4.23321 2.30384e-05
## ...                    ...            ...       ...       ...         ...
## ENSG00000273294 18.6100019      -0.553297  0.866688 -0.638404   0.5232107
## ENSG00000273331  1.6135503       2.834898  1.244909  2.277193   0.0227747
## ENSG00000273398  5.7510137      -0.419677  0.607245 -0.691117   0.4894919
## ENSG00000273439 22.4666648      -0.072854  0.470010 -0.155005   0.8768173
## ENSG00000273452  0.0657423       2.226346  5.458155  0.407893   0.6833519
##                        padj
##                   <numeric>
## ENSG00000000003 2.17650e-03
## ENSG00000000005 3.16468e-05
## ENSG00000000419 1.08888e-02
## ENSG00000000457 3.44937e-03
## ENSG00000000460 7.32642e-05
## ...                     ...
## ENSG00000273294   0.6021114
## ENSG00000273331   0.0401088
## ENSG00000273398   0.5705074
## ENSG00000273439   0.9009092
## ENSG00000273452   0.7426654
resLFC_Ascites <- lfcShrink(dds, coef="Tissue_Ascites_vs_Benign_Tissue", type="apeglm")
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
##     sequence count data: removing the noise and preserving large differences.
##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
resLFC_Ascites
## log2 fold change (MAP): Tissue Ascites vs Benign Tissue 
## Wald test p-value: Tissue Ascites vs Benign Tissue 
## DataFrame with 19975 rows and 5 columns
##                   baseMean log2FoldChange     lfcSE      pvalue        padj
##                  <numeric>      <numeric> <numeric>   <numeric>   <numeric>
## ENSG00000000003  1359.4263      -0.837964  0.267011 9.13167e-04 2.17650e-03
## ENSG00000000005    28.6264      -0.844550  0.833044 9.25110e-06 3.16468e-05
## ENSG00000000419   945.1298       0.475800  0.173115 5.33562e-03 1.08888e-02
## ENSG00000000457   339.0323      -0.441299  0.139981 1.50761e-03 3.44937e-03
## ENSG00000000460   189.3951       1.050195  0.259593 2.30384e-05 7.32642e-05
## ...                    ...            ...       ...         ...         ...
## ENSG00000273294 18.6100019     -0.4558388  0.684649   0.5232107   0.6021114
## ENSG00000273331  1.6135503      0.8943902  1.171554   0.0227747   0.0401088
## ENSG00000273398  5.7510137     -0.5741662  0.558885   0.4894919   0.5705074
## ENSG00000273439 22.4666648     -0.1303708  0.426959   0.8768173   0.9009092
## ENSG00000273452  0.0657423      0.0804353  1.022513   0.6833519   0.7426654

Using lfcShrik to make vizualization and ranking of genes better.

resLFCOrdered_Serous_EOC <- resLFC_Serous_EOC[order(resLFC_Serous_EOC$pvalue),]

resLFCOrdered_Serous_EOC
## log2 fold change (MAP): Tissue Serous EOC vs Benign Tissue 
## Wald test p-value: Tissue Serous EOC vs Benign Tissue 
## DataFrame with 19975 rows and 5 columns
##                  baseMean log2FoldChange     lfcSE      pvalue        padj
##                 <numeric>      <numeric> <numeric>   <numeric>   <numeric>
## ENSG00000100380 5724.9650       -2.36653  0.160123 6.74717e-50 9.08323e-46
## ENSG00000154545   61.4084       11.23218  3.529445 9.46367e-50 9.08323e-46
## ENSG00000100227 1539.3231       -1.92776  0.143622 2.46081e-41 1.57459e-37
## ENSG00000139734  173.2278        4.27102  0.321202 9.45548e-41 4.53768e-37
## ENSG00000069966  566.3977       -2.15348  0.163485 4.28627e-40 1.64559e-36
## ...                   ...            ...       ...         ...         ...
## ENSG00000184007   3630.87    0.000342523  0.127981    0.999963    0.999963
## ENSG00000154537      0.00    0.069290955  0.887184    1.000000          NA
## ENSG00000242366      0.00    0.061543666  0.885882    1.000000          NA
## ENSG00000255863      0.00    0.071625991  0.887520    1.000000          NA
## ENSG00000268485      0.00    0.068737657  0.887100    1.000000          NA
resLFCOrdered_Ascites <- resLFC_Ascites[order(resLFC_Ascites$pvalue),]

resLFCOrdered_Ascites
## log2 fold change (MAP): Tissue Ascites vs Benign Tissue 
## Wald test p-value: Tissue Ascites vs Benign Tissue 
## DataFrame with 19975 rows and 5 columns
##                  baseMean log2FoldChange     lfcSE      pvalue        padj
##                 <numeric>      <numeric> <numeric>   <numeric>   <numeric>
## ENSG00000115461 16817.080       -6.76674  0.420994 4.15654e-58 8.30102e-54
## ENSG00000100380  5724.965       -2.74955  0.178141 2.86234e-54 2.85819e-50
## ENSG00000072840   769.001       -3.36303  0.223246 7.75342e-52 5.16145e-48
## ENSG00000100227  1539.323       -2.41606  0.161322 2.93616e-51 1.46595e-47
## ENSG00000113658  1041.526       -2.28680  0.158517 1.05393e-47 4.20962e-44
## ...                   ...            ...       ...         ...         ...
## ENSG00000147596   3.33851     -0.1884468  0.527538    0.999197    0.999197
## ENSG00000154537   0.00000     -0.0302969  1.017772    1.000000          NA
## ENSG00000242366   0.00000     -0.0277997  1.017821    1.000000          NA
## ENSG00000255863   0.00000     -0.0311519  1.017759    1.000000          NA
## ENSG00000268485   0.00000     -0.0301904  1.017780    1.000000          NA

Ordered by p-value.

summary(resLFCOrdered_Serous_EOC)
## 
## out of 19971 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 6618, 33%
## LFC < 0 (down)     : 4518, 23%
## outliers [1]       : 0, 0%
## low counts [2]     : 779, 3.9%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
summary(resLFCOrdered_Ascites)
## 
## out of 19971 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 6636, 33%
## LFC < 0 (down)     : 6123, 31%
## outliers [1]       : 0, 0%
## low counts [2]     : 4, 0.02%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results

MA Plots

plotMA(resLFCOrdered_Serous_EOC, ylim=c(-2,2))

idx_LFCOrdered_Serous_EOC <- identify(resLFCOrdered_Serous_EOC$baseMean, resLFCOrdered_Serous_EOC$log2FoldChange)

rownames(resLFCOrdered_Serous_EOC)[idx_LFCOrdered_Serous_EOC]
## character(0)
plotMA(resLFCOrdered_Ascites, ylim=c(-2,2))

plotMA(resLFCOrdered_Serous_EOC, ylim=c(-2,2))

idx_LFCOrdered_Serous_EOC <- identify(resLFCOrdered_Serous_EOC$baseMean, resLFCOrdered_Serous_EOC$log2FoldChange)

rownames(resLFCOrdered_Serous_EOC)[idx_LFCOrdered_Serous_EOC]
## character(0)
resNorm <- lfcShrink(dds, coef=3, type="normal")
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
resAsh <- lfcShrink(dds, coef=3, type="ashr")
## using 'ashr' for LFC shrinkage. If used in published research, please cite:
##     Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
##     https://doi.org/10.1093/biostatistics/kxw041
resLFC <- lfcShrink(dds, coef=3, type="apeglm")
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
##     sequence count data: removing the noise and preserving large differences.
##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
par(mfrow=c(1,3), mar=c(4,4,2,1))
xlim <- c(1,1e5); ylim <- c(-3,3)
plotMA(resLFC, xlim=xlim, ylim=ylim, main="apeglm")
plotMA(resNorm, xlim=xlim, ylim=ylim, main="normal")
plotMA(resAsh, xlim=xlim, ylim=ylim, main="ashr")

Plot Counts

plotCounts(dds, gene=which.min(resLFC_Serous_EOC$padj), intgroup="Tissue")

plotCounts(dds, gene=which.min(resLFC_Ascites$padj), intgroup="Tissue")

plotCounts(dds, gene=which.max(resLFC_Serous_EOC$padj), intgroup="Tissue")

plotCounts(dds, gene=which.max(resLFC_Ascites$padj), intgroup="Tissue")

Data Transformations + Variance

vsd <- vst(dds, blind=FALSE)
#rld <- rlog(dds, blind=FALSE)
ntd <- normTransform(dds)
library("vsn")
head(assay(vsd), 10)
##                 SRR10912052 SRR10912053 SRR10912054 SRR10912055 SRR10912056
## ENSG00000000003    9.681800   11.409086   10.373103   10.861253    9.804261
## ENSG00000000005    5.657905    7.242788    7.324413    5.657905    5.657905
## ENSG00000000419   10.143497    9.959867   10.159948   10.482950   10.201271
## ENSG00000000457    8.778506    8.722905    8.701035    8.582886    8.955522
## ENSG00000000460    8.362459    7.709567    8.302766    7.852794    7.959261
## ENSG00000000938    8.780148    8.866326    8.220189    6.892303    8.679407
## ENSG00000000971   11.347021   12.181838   12.121527    9.671775   13.727215
## ENSG00000001036   10.324587   10.764334   10.538936    9.731350   10.829804
## ENSG00000001084    8.812592    9.807770    9.398510    9.429871    9.110757
## ENSG00000001167    9.789864    9.155460    9.201835    9.320337    9.062793
##                 SRR10912057 SRR10912058 SRR10912059 SRR10912060 SRR10912061
## ENSG00000000003   10.682838    9.112636    8.461819   10.639874   10.202538
## ENSG00000000005    5.834671    6.058665    5.657905    6.745606    5.657905
## ENSG00000000419   10.023898    9.791563    9.282585   10.369044    9.815419
## ENSG00000000457    8.986655    8.742551    8.358014    8.744751    8.705403
## ENSG00000000460    8.710638    7.591303    7.993237    7.186378    5.657905
## ENSG00000000938    7.648104   10.074286   10.837538    7.853983    8.663492
## ENSG00000000971   11.194760   13.162126   11.148968   12.214480   11.413431
## ENSG00000001036   10.445657   10.926095   11.153241   10.424914   10.027705
## ENSG00000001084    9.669354    9.722022    9.641572    8.383807    8.633388
## ENSG00000001167    9.844475    9.114695    8.595770    9.295579    9.002105
##                 SRR10912062 SRR10912063 SRR10912064 SRR10912065 SRR10912066
## ENSG00000000003   10.479004    9.696886   11.402766   10.635603   10.662796
## ENSG00000000005    5.657905    6.540222    5.657905    6.048287    6.043689
## ENSG00000000419    9.998549    9.591109    9.994770    9.665286   10.498476
## ENSG00000000457    7.540698    8.059312    9.196570    9.322005    8.367355
## ENSG00000000460    8.117656    8.041946    8.708845    8.244808    8.577920
## ENSG00000000938    8.856192    9.497592    8.243940    8.463016    6.875277
## ENSG00000000971   12.171610   12.334648   12.146060   12.311195   10.779275
## ENSG00000001036   10.932153   10.660764   10.754683   10.506151   10.429774
## ENSG00000001084    9.437551   10.370967    9.628905    9.583375    9.188028
## ENSG00000001167    9.064419    9.085622    9.245514    9.145594    9.537424
##                 SRR10912067 SRR10912068 SRR10912069 SRR10912070 SRR10912071
## ENSG00000000003    9.514325    9.748332    8.928942   10.655050    9.182824
## ENSG00000000005    6.062590    6.024113    6.033176    5.906723    5.966233
## ENSG00000000419   10.218577   10.090740    9.729964   10.343107   10.317958
## ENSG00000000457    8.856011    8.879201    8.956091    8.810905    8.523476
## ENSG00000000460    7.857643    8.024855    7.788848    8.499530    7.790599
## ENSG00000000938   11.264581    9.000551   10.138362    7.652670    9.462310
## ENSG00000000971   10.352780   11.321204   11.565934   11.378536   13.652808
## ENSG00000001036   10.493664   10.356254   10.325404   10.304064   10.271805
## ENSG00000001084   10.077249    9.041806    9.421800    9.444026    9.161434
## ENSG00000001167    9.114372    9.555633    9.289950    9.149040    8.936867
##                 SRR10912072 SRR10912073 SRR10912074 SRR10912075 SRR10912076
## ENSG00000000003    9.789805   11.805167   10.369948    9.528750    9.369386
## ENSG00000000005    5.859382    6.659314    7.349422    5.657905    7.217420
## ENSG00000000419    9.869783   10.405773   10.355734    9.860681    9.958212
## ENSG00000000457    8.597413    9.256500    9.136575    9.553261    9.034634
## ENSG00000000460    8.500700    8.517670    8.866012    8.830151    7.911606
## ENSG00000000938    8.691349    6.881988    8.925536    8.722829    9.153147
## ENSG00000000971   12.707167    8.275037   10.985326   10.365997   12.363049
## ENSG00000001036   10.318118    9.742067   10.369948    9.985780    9.956461
## ENSG00000001084    9.152122    9.668891    9.778350    9.639703    9.747536
## ENSG00000001167    9.206526   10.435005    9.878489    9.632057    9.041300
##                 SRR10912077 SRR10912078 SRR10912079 SRR10912080 SRR10912081
## ENSG00000000003    9.237389   11.686149   11.652404   10.949399    9.884800
## ENSG00000000005    6.335134    7.790812    6.513903    5.657905    5.853438
## ENSG00000000419    9.695735   10.505656   10.398156   11.690905   10.842806
## ENSG00000000457    8.858825    8.695915    8.726438    8.675206    8.720754
## ENSG00000000460    7.905158    8.464077    7.902284    8.496152    7.790266
## ENSG00000000938    9.729510    7.692708    7.216529    7.834771    9.635456
## ENSG00000000971   12.557434   11.256344   12.572694   11.313913   12.410532
## ENSG00000001036   10.501549   10.498353   10.290944   11.849828   11.387249
## ENSG00000001084    9.420927    9.000285    9.335599   10.360706    9.871949
## ENSG00000001167    9.121185   10.372169    9.683824    9.518110    8.876175
##                 SRR10912082 SRR10912083 SRR10912084 SRR10912085 SRR10912086
## ENSG00000000003   10.731144   10.626092   10.391571    9.856398   10.063216
## ENSG00000000005    5.657905    5.657905    5.657905    8.312581    9.890164
## ENSG00000000419   10.106190   10.459343   10.316807   10.277954    9.932346
## ENSG00000000457    9.471265    8.715264    8.799716    8.959380    8.697414
## ENSG00000000460    9.322198    8.184746    8.685387    7.888386    7.847349
## ENSG00000000938    8.230518    7.908309    8.739724    8.987737    9.060709
## ENSG00000000971   11.376767   12.608898   11.601021   13.279539   12.940010
## ENSG00000001036    9.516211   10.019321   10.552999   10.455881   10.626832
## ENSG00000001084    9.980664    9.376255    9.843706    9.739313    9.748638
## ENSG00000001167   10.132336    9.979692    9.969251    9.828365    8.866547
##                 SRR10912087 SRR10912088 SRR10912089 SRR10912090 SRR10912091
## ENSG00000000003   10.120827   10.632332   10.750688   10.123521    9.854531
## ENSG00000000005    8.319678    6.594620    6.053124    5.839106    6.584189
## ENSG00000000419   10.170484   10.062837    9.774579    9.751626    9.564163
## ENSG00000000457    9.112089    9.391028    8.996665    8.632326    8.715865
## ENSG00000000460    7.547950    8.538632    9.095365    7.636814    7.694398
## ENSG00000000938   10.262584    7.579326    7.497154    7.862851    7.712670
## ENSG00000000971   12.348515   10.132630    9.212140   11.335545   12.672983
## ENSG00000001036   10.334697   10.124089   11.204638   10.019598   10.092598
## ENSG00000001084   10.053189    9.559337    9.471102    8.982612    8.662969
## ENSG00000001167    9.244730    9.388645    9.431611    8.882670    8.772389
##                 SRR10912092 SRR10912093 SRR10912094 SRR10912095 SRR10912096
## ENSG00000000003    9.690714    9.773876    9.579622   10.102560   10.589301
## ENSG00000000005    8.596961    5.657905    5.888589    5.909040    5.966965
## ENSG00000000419    9.457148   10.056925    9.664049   10.200022    9.631575
## ENSG00000000457    8.509949    9.444754    8.808824    8.746192    8.825123
## ENSG00000000460    7.657982    8.724068    8.342910    7.990393    8.378910
## ENSG00000000938    9.221176    7.118030    7.965176    7.540845    7.778890
## ENSG00000000971   12.888248    9.997007    8.607472   11.344746   10.803650
## ENSG00000001036   10.539513    9.980753    9.948034   10.236310    9.962584
## ENSG00000001084    9.564194    8.540289    9.090626    9.241295    9.513569
## ENSG00000001167    9.260549    9.147596    8.697005    9.533242    9.597184
##                 SRR10912097 SRR10912098 SRR10912099 SRR10912100 SRR10912101
## ENSG00000000003   11.917693   10.727720   11.769118   10.730239   10.127653
## ENSG00000000005    6.775833    5.840411    5.657905    5.657905    5.657905
## ENSG00000000419    9.469495    9.354318   10.179571    9.974597    9.538607
## ENSG00000000457    8.889644    8.642477    9.182089    8.884587    8.232557
## ENSG00000000460    7.459067    7.679849    8.961568    8.881745    7.475655
## ENSG00000000938    6.978535    7.844592    6.463177    7.574433    7.505389
## ENSG00000000971   12.436809   10.611550    7.538526   10.600251   11.531618
## ENSG00000001036   11.046980    9.343513   10.749000    9.609189    9.802213
## ENSG00000001084    8.017013    8.703462   10.052257    9.155124    9.037496
## ENSG00000001167    9.456906    9.026969   10.028862    9.384264    9.795277
##                 SRR10912102 SRR10912103 SRR10912104 SRR10912105 SRR10912106
## ENSG00000000003    9.388029   10.768546   10.662914   10.441764   10.054127
## ENSG00000000005    8.013932    5.897503    6.325378    5.913889    6.284343
## ENSG00000000419    9.048870   11.036591   10.505685    9.626125    9.585915
## ENSG00000000457    8.615069    8.894731    8.733927    8.903725    8.604968
## ENSG00000000460    7.241883    9.250399    8.465158    7.981010    8.345257
## ENSG00000000938    9.351160    7.312097    8.292841    7.846434    7.805776
## ENSG00000000971   12.581507    8.971749   10.120269    9.127736   10.652037
## ENSG00000001036   10.216597   10.879143   10.023739   10.264039    9.860845
## ENSG00000001084    9.866794    9.628368    9.676119   10.030685   10.014120
## ENSG00000001167    8.979839    9.641085    9.898180    9.866292    9.464691
##                 SRR10912107 SRR10912108 SRR10912109 SRR10912110 SRR10912111
## ENSG00000000003    9.838507    9.999543   11.060003   10.957206    9.354553
## ENSG00000000005    6.397608    5.964393    6.371779    6.703043    5.977341
## ENSG00000000419    9.685932    9.556288    9.419346    9.590830   10.376476
## ENSG00000000457    8.182953    9.154727    8.625538    8.899580    8.966275
## ENSG00000000460    7.941163    8.197571    7.698895    8.043477    8.730311
## ENSG00000000938    8.566607    7.953876    7.563975    7.013731    7.760243
## ENSG00000000971    8.939289   10.077371   10.494718    9.782016    9.302175
## ENSG00000001036    9.663395   10.297584   10.618685    9.724768   10.291157
## ENSG00000001084    9.248075    9.322414    9.171858   10.042092    8.826827
## ENSG00000001167    8.959294    9.278970    9.378679    9.528836    9.958276
##                 SRR10912112 SRR10912113 SRR10912114 SRR10912115 SRR10912116
## ENSG00000000003   10.549125   10.169169    9.996266   11.065692   10.902300
## ENSG00000000005    5.657905    6.326860    6.035439    6.244687    5.657905
## ENSG00000000419    9.646125   10.161344   10.365855   10.019270    9.938859
## ENSG00000000457    9.053949    8.848088    8.596867    8.958203    8.876283
## ENSG00000000460    8.898865    8.496749    8.227468    8.459481    7.870837
## ENSG00000000938    7.434567    7.971937    7.364498    7.415554    7.100761
## ENSG00000000971    9.592380   10.305563    9.928723   10.170340   10.783769
## ENSG00000001036   10.057250   10.444526   10.124312    9.484405    9.825351
## ENSG00000001084    9.106949    9.324805    9.535727    8.553655    8.934686
## ENSG00000001167    9.230434   10.039492   10.144881    9.996473    9.352783
##                 SRR10912117 SRR10912118 SRR10912119 SRR10912120 SRR10912121
## ENSG00000000003   10.870113    9.791178   10.165048   10.569699    9.015377
## ENSG00000000005    5.857542    8.312728    6.100406    5.657905    5.657905
## ENSG00000000419   10.148289    8.973836    9.788153   10.042770   11.134821
## ENSG00000000457    8.950647    8.810180    8.608425    8.703128    8.566547
## ENSG00000000460    8.631807    7.705391    8.511551    8.292418    8.355014
## ENSG00000000938    7.420482    8.996380    8.176935    7.730380    8.085127
## ENSG00000000971    9.764560   11.878304   11.328730   11.961958   10.352330
## ENSG00000001036   10.424611    9.963537   10.097171   10.274575   10.198448
## ENSG00000001084    9.965713    9.548955    9.873345    8.995594    9.416324
## ENSG00000001167    9.134977    8.899180   10.474080    9.634069    8.879204
##                 SRR10912122 SRR10912123 SRR10912124 SRR10912125 SRR10912126
## ENSG00000000003   10.032690   10.087136    9.571070   10.769773    9.998517
## ENSG00000000005    5.917593    5.846894    5.657905    6.315303    6.083379
## ENSG00000000419    9.792948   10.325678    9.571070    9.705033    9.722388
## ENSG00000000457    8.428563    8.588788    8.249535    8.807468    8.599225
## ENSG00000000460    8.240850    8.653428    7.277281    8.350049    7.800779
## ENSG00000000938    7.679525    6.975433   10.694501    7.674585    8.072405
## ENSG00000000971   11.161452   11.393834   12.002600   11.354242   11.397260
## ENSG00000001036   10.541345   10.350856   10.290530   10.721072   10.140321
## ENSG00000001084    9.287902    9.326182    9.759159    9.412767    8.685759
## ENSG00000001167    9.587543    9.413703    9.824346    9.512946    8.729642
##                 SRR10912127 SRR10912128 SRR10912129 SRR10912130 SRR10912131
## ENSG00000000003   11.547610   11.424025   10.516266   11.203384   11.180243
## ENSG00000000005    7.042143    6.323793    7.599408    6.528323    6.551915
## ENSG00000000419    9.701540    9.541748    9.518293    9.632376    9.832567
## ENSG00000000457    9.114526    8.892260    8.684853    8.941324    8.812885
## ENSG00000000460    7.324757    7.774050    7.157379    7.359458    7.265680
## ENSG00000000938    6.771086    7.184867    7.623889    6.985543    6.601337
## ENSG00000000971   12.267779   12.696265   13.118564   12.085980   13.146398
## ENSG00000001036   10.692839   10.466388   10.706315   10.629241   10.610018
## ENSG00000001084    9.104741    9.686723    8.441543    8.613121    8.722025
## ENSG00000001167    9.628640    9.590581    9.300408    9.290722    9.664211
##                 SRR10912132 SRR10912133 SRR10912134 SRR10912135 SRR10912136
## ENSG00000000003   11.088528   11.352780   11.561319   11.162601   11.080903
## ENSG00000000005    8.238922    6.339365    6.550300    6.678743    7.238449
## ENSG00000000419    9.625747    9.802675    9.827910    9.769506    9.699860
## ENSG00000000457    8.813425    9.015513    9.136782    9.105001    8.784260
## ENSG00000000460    7.458393    7.507021    7.670911    7.478916    7.214206
## ENSG00000000938    8.016745    6.866548    6.405736    6.888255    6.576864
## ENSG00000000971   12.678047   13.099437   12.717134   12.577411   12.899147
## ENSG00000001036   10.349596   10.100810   10.584969   10.678041   10.562391
## ENSG00000001084    8.974784    8.976818    9.283510    8.705346    8.533921
## ENSG00000001167    9.426759    9.643702    9.801436    9.741259    9.475391
##                 SRR10912137 SRR10912138 SRR10912139 SRR10912140 SRR10912141
## ENSG00000000003   11.203735   10.462749   10.558232    9.352170   10.087283
## ENSG00000000005    7.240333    5.657905    5.876671    5.813607    5.927808
## ENSG00000000419    9.615981   10.624721   10.246869    9.749570   10.352192
## ENSG00000000457    8.866021    8.297502    8.579115    8.191324    8.416318
## ENSG00000000460    7.369684    7.890839    8.262644    7.675062    8.050090
## ENSG00000000938    6.577731    9.096736    8.967233    9.853455    9.511546
## ENSG00000000971   12.715642   11.472347    9.754289   11.011983   10.036134
## ENSG00000001036   10.475592   10.888224   10.426827   10.865948    9.660181
## ENSG00000001084    8.320703    8.781438    9.308742    9.567728    9.451031
## ENSG00000001167    9.782998    9.436212    9.320109    9.178360    9.010589
##                 SRR10912142 SRR10912143 SRR10912144 SRR10912145 SRR10912146
## ENSG00000000003    9.486470   11.422991   10.814979    9.913147   10.431310
## ENSG00000000005    6.957346    6.033677    5.657905    5.727410    6.085379
## ENSG00000000419    9.403874   10.087039    9.755601    9.566692   10.049713
## ENSG00000000457    8.619509    9.217822    9.031900    9.129272    8.795796
## ENSG00000000460    7.402051    8.392672    7.177641    8.066668    8.656107
## ENSG00000000938   10.139142    8.430335    8.337217    8.964144    8.971527
## ENSG00000000971   12.144935   10.191008   11.952959    9.673892    9.778397
## ENSG00000001036   10.341637   10.420020   10.289594   10.281137   10.391436
## ENSG00000001084    9.295030    8.900146    9.200154    9.180694    9.221930
## ENSG00000001167    8.899336    9.471624    9.227674    9.078129   10.028288
##                 SRR10912147 SRR10912148 SRR10912149 SRR10912150 SRR10912151
## ENSG00000000003   10.984098   11.419437   10.896370    9.860183    9.919823
## ENSG00000000005    6.700062    6.226983    5.657905    5.657905    9.117115
## ENSG00000000419   10.679558   10.030078   10.530196    9.784860   10.341940
## ENSG00000000457    8.633689    8.824891    8.475233    8.598698    8.898314
## ENSG00000000460    7.395527    8.772661    7.720103    7.824260    6.559439
## ENSG00000000938    6.700062    7.824077    8.083384    7.539390    8.799438
## ENSG00000000971    9.338240    8.928647   11.302693   11.403038   12.637810
## ENSG00000001036    9.715562   10.989807   10.136391    9.704060   10.482927
## ENSG00000001084    9.067209    9.228072    9.115244    8.555554    9.745390
## ENSG00000001167    8.152816    8.548046    7.949802    8.271179    8.668850
##                 SRR10912152 SRR10912153 SRR10912154 SRR10912155 SRR10912156
## ENSG00000000003   10.078535    9.852815   10.388402   10.079501   10.611783
## ENSG00000000005    5.657905    5.657905    5.657905    5.911852    5.657905
## ENSG00000000419   10.259810    9.957853   10.290172   10.147716    9.596196
## ENSG00000000457    8.836992    8.558326    9.535682    8.058282    8.434271
## ENSG00000000460    8.548649    8.248089    7.897485    7.859239    7.377141
## ENSG00000000938    7.454627    7.996913    8.775385    8.859813    9.795498
## ENSG00000000971    9.647801    8.380668    9.766278   10.540512   10.740995
## ENSG00000001036   10.034946    9.980854   10.135844   10.385985    9.278702
## ENSG00000001084    8.153185    8.961764    8.649668    9.824866    8.309312
## ENSG00000001167    8.299080    8.384380    8.473338    8.762909    8.366951
##                 SRR10912157 SRR10912158 SRR10912159 SRR10912160 SRR10912161
## ENSG00000000003   11.795582   11.389175    7.463260    8.449949    5.657905
## ENSG00000000005    5.907736    5.657905    5.657905    5.657905    5.657905
## ENSG00000000419   10.287970   10.563573    9.092711    9.122085    5.657905
## ENSG00000000457    8.777955    8.552365    6.973376    8.080911    5.657905
## ENSG00000000460    8.639941    8.412397    7.810745    7.763193    5.657905
## ENSG00000000938    6.290565    7.764174   11.197375   11.934724   11.921847
## ENSG00000000971    7.195523    8.474792   11.224753   10.904849   10.797183
## ENSG00000001036   10.550255    9.306829   10.587098   10.629538   10.797183
## ENSG00000001084    9.542411    9.288514    8.311946    8.905904    9.011239
## ENSG00000001167    8.875595    8.397589    8.830090    8.740883    5.657905
##                 SRR10912162 SRR10912163 SRR10912164 SRR10912165 SRR10912166
## ENSG00000000003    7.943794   10.470963   10.605714   10.772655    5.657905
## ENSG00000000005    5.657905    5.657905    5.657905    5.657905    5.657905
## ENSG00000000419    9.325488   10.749076   10.105470    9.926235    5.657905
## ENSG00000000457    8.664924    8.612039    7.967889    8.958865    5.657905
## ENSG00000000460    8.351213    7.970324    8.427629    8.506545    5.657905
## ENSG00000000938   10.214300    8.446406    7.254249    6.790146   11.211340
## ENSG00000000971   10.116392    8.700865    8.427629    8.212602   11.211340
## ENSG00000001036   10.305962   10.482355    9.651894    9.800036   11.211340
## ENSG00000001084    8.920954    9.674335    8.846012    8.745098    5.657905
## ENSG00000001167    8.664924    9.544415    9.331168    9.379211    5.657905
##                 SRR10912167 SRR10912168 SRR10912169 SRR10912170 SRR10912171
## ENSG00000000003    8.180514   10.547861    8.892073   10.261833   10.917607
## ENSG00000000005    5.657905    5.657905    5.657905    5.833176    6.128923
## ENSG00000000419    8.975025    9.892837   10.827876    9.939263   10.724170
## ENSG00000000457    8.521530    8.840929    8.410319    8.561306    8.410402
## ENSG00000000460    7.517007    7.740980    8.389061    8.253949    8.627841
## ENSG00000000938   12.081720    8.816368    8.525899    7.856368    6.977212
## ENSG00000000971   11.537685   10.113472    8.329650    9.293253    9.651555
## ENSG00000001036   10.560302    9.520768    9.799506   10.599937   10.217742
## ENSG00000001084    8.597257    8.543741    9.788173    9.166928    9.180999
## ENSG00000001167    9.195511    9.555541    8.323244    9.571613    8.751902
##                 SRR10912172
## ENSG00000000003    8.903937
## ENSG00000000005    5.657905
## ENSG00000000419    9.165333
## ENSG00000000457    8.142573
## ENSG00000000460    7.627294
## ENSG00000000938   11.488695
## ENSG00000000971   10.114272
## ENSG00000001036   10.389456
## ENSG00000001084    9.028052
## ENSG00000001167    8.914531
meanSdPlot(assay(ntd))

meanSdPlot(assay(vsd))

#meanSdPlot(assay(rld))

Heatmaps

library("pheatmap")
select <- order(rowMeans(counts(dds,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(dds)[,c("Tissue","Treatment")])
pheatmap(assay(ntd)[select,], cluster_rows=FALSE, show_rownames=FALSE,
         cluster_cols=FALSE, annotation_col=df)

pheatmap(assay(vsd)[select,], cluster_rows=FALSE, show_rownames=FALSE,
         cluster_cols=FALSE, annotation_col=df)

#pheatmap(assay(rld)[select,], cluster_rows=FALSE, show_rownames=FALSE,
#         cluster_cols=FALSE, annotation_col=df)
sampleDists <- dist(t(assay(vsd)))

library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsd$condition, vsd$type, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)

PCA

plotPCA(vsd, intgroup=c("Tissue"))

Random

results(dds, contrast=c("Tissue","Serous_EOC","Benign_Tissue"))
## log2 fold change (MLE): Tissue Serous_EOC vs Benign_Tissue 
## Wald test p-value: Tissue Serous EOC vs Benign Tissue 
## DataFrame with 19975 rows and 6 columns
##                   baseMean log2FoldChange     lfcSE      stat      pvalue
##                  <numeric>      <numeric> <numeric> <numeric>   <numeric>
## ENSG00000000003  1359.4263      -0.790170  0.248072 -3.185252 1.44628e-03
## ENSG00000000005    28.6264      -0.512972  0.913941 -0.561275 5.74610e-01
## ENSG00000000419   945.1298       0.408815  0.156660  2.609561 9.06584e-03
## ENSG00000000457   339.0323      -0.139602  0.124784 -1.118752 2.63246e-01
## ENSG00000000460   189.3951       1.346737  0.231249  5.823762 5.75375e-09
## ...                    ...            ...       ...       ...         ...
## ENSG00000273294 18.6100019       0.115738  0.774778 0.1493822    0.881252
## ENSG00000273331  1.6135503       2.457218  1.142924 2.1499398    0.031560
## ENSG00000273398  5.7510137       0.562653  0.536606 1.0485403    0.294390
## ENSG00000273439 22.4666648       0.669630  0.415259 1.6125583    0.106841
## ENSG00000273452  0.0657423       0.197680  4.954781 0.0398967    0.968175
##                        padj
##                   <numeric>
## ENSG00000000003 3.91623e-03
## ENSG00000000005 6.64751e-01
## ENSG00000000419 1.99003e-02
## ENSG00000000457 3.58693e-01
## ENSG00000000460 5.14674e-08
## ...                     ...
## ENSG00000273294   0.9141591
## ENSG00000273331   0.0593248
## ENSG00000273398   0.3925197
## ENSG00000273439   0.1682453
## ENSG00000273452          NA

More LFC Shrinkage

resApeT <- lfcShrink(dds, coef=2, type="apeglm", lfcThreshold=1)
## using 'apeglm' for LFC shrinkage. If used in published research, please cite:
##     Zhu, A., Ibrahim, J.G., Love, M.I. (2018) Heavy-tailed prior distributions for
##     sequence count data: removing the noise and preserving large differences.
##     Bioinformatics. https://doi.org/10.1093/bioinformatics/bty895
## computing FSOS 'false sign or small' s-values (T=1)
plotMA(resApeT, ylim=c(-3,3), cex=.8)
## thresholding s-values on alpha=0.005 to color points
abline(h=c(-1,1), col="dodgerblue", lwd=2)

resAshT <- lfcShrink(dds, coef=2, type="ashr", lfcThreshold=1)
## using 'ashr' for LFC shrinkage. If used in published research, please cite:
##     Stephens, M. (2016) False discovery rates: a new deal. Biostatistics, 18:2.
##     https://doi.org/10.1093/biostatistics/kxw041
## computing FSOS 'false sign or small' s-values (T=1)
plotMA(resAshT, ylim=c(-3,3), cex=.8)
## thresholding s-values on alpha=0.005 to color points
abline(h=c(-1,1), col="dodgerblue", lwd=2)

Boxplot

par(mar=c(8,5,2,2))
boxplot(log10(assays(dds)[["cooks"]]), range=0, las=2)

Dispersion Plot

plotDispEsts(dds)

Other

metadata(resLFC_Serous_EOC)$alpha
## [1] 0.1
metadata(resLFC_Serous_EOC)$filterThreshold
## 3.896759% 
## 0.2997622
plot(metadata(resLFC_Serous_EOC)$filterNumRej, 
     type="b", ylab="number of rejections",
     xlab="quantiles of filter")
lines(metadata(resLFC_Serous_EOC)$lo.fit, col="red")
abline(v=metadata(resLFC_Serous_EOC)$filterTheta)

resNoFilt <- results(dds, independentFiltering=FALSE)
addmargins(table(filtering=(resLFC_Serous_EOC$padj < .1),
                 noFiltering=(resNoFilt$padj < .1)))
##          noFiltering
## filtering FALSE  TRUE   Sum
##     FALSE  8060     0  8060
##     TRUE     84 11052 11136
##     Sum    8144 11052 19196
metadata(resLFC_Ascites)$alpha
## [1] 0.1
metadata(resLFC_Ascites)$filterThreshold
## 0.02002503% 
## 0.005648919
plot(metadata(resLFC_Ascites)$filterNumRej, 
     type="b", ylab="number of rejections",
     xlab="quantiles of filter")
lines(metadata(resLFC_Ascites)$lo.fit, col="red")
abline(v=metadata(resLFC_Ascites)$filterTheta)

resNoFilt <- results(dds, independentFiltering=FALSE)
addmargins(table(filtering=(resLFC_Ascites$padj < .1),
                 noFiltering=(resNoFilt$padj < .1)))
##          noFiltering
## filtering FALSE  TRUE   Sum
##     FALSE  5118  2094  7212
##     TRUE   3801  8958 12759
##     Sum    8919 11052 19971
par(mfrow=c(2,2),mar=c(2,2,1,1))
ylim <- c(-2.5,2.5)
resGA <- results(dds, lfcThreshold=.5, altHypothesis="greaterAbs")
resLA <- results(dds, lfcThreshold=.5, altHypothesis="lessAbs")
resG <- results(dds, lfcThreshold=.5, altHypothesis="greater")
resL <- results(dds, lfcThreshold=.5, altHypothesis="less")
drawLines <- function() abline(h=c(-.5,.5),col="dodgerblue",lwd=2)
plotMA(resGA, ylim=ylim); drawLines()
plotMA(resLA, ylim=ylim); drawLines()
plotMA(resG, ylim=ylim); drawLines()
plotMA(resL, ylim=ylim); drawLines()

#mcols(dds,use.names=TRUE)[1:4,1:4]

#substr(names(mcols(dds)),1,10) 

#mcols(mcols(dds), use.names=TRUE)[1:4,]

#head(assays(dds)[["mu"]])

#head(assays(dds)[["cooks"]])

#head(dispersions(dds))

#head(mcols(dds)$dispersion)

#sizeFactors(dds)

#head(coef(dds))

#attr(dds, "betaPriorVar")

#priorInfo(resLFC)
library("AnnotationDbi")
library("org.Hs.eg.db")
## 
final_data_Serous <- read.csv('Final_Data_LFC_Serous_EOC.csv')

final_data_Serous$symbol <- mapIds(org.Hs.eg.db, keys = final_data_Serous$X, column = 'SYMBOL', keytype = 'ENSEMBL')
## 'select()' returned 1:many mapping between keys and columns
final_data_Serous <- final_data_Serous[order(final_data_Serous$pvalue),]

head(final_data_Serous, 50)
##                     X   baseMean log2FoldChange     lfcSE       pvalue
## 2280  ENSG00000100380 5724.96499      -2.366535 0.1601225 6.747169e-50
## 9682  ENSG00000154545   61.40843      11.232181 3.5294452 9.463669e-50
## 2216  ENSG00000100227 1539.32309      -1.927756 0.1436224 2.460812e-41
## 7803  ENSG00000139734  173.22782       4.271019 0.3212016 9.455479e-41
## 1090  ENSG00000069966  566.39767      -2.153479 0.1634850 4.286271e-40
## 16378 ENSG00000196839  229.11553       2.378881 0.1868901 8.342579e-38
## 1770  ENSG00000088325 1169.30542       4.383619 0.3511531 1.148710e-37
## 7554  ENSG00000138180  416.12170       4.765852 0.3811779 2.852664e-37
## 18107 ENSG00000215298   33.08025      10.777277 4.0612932 6.886416e-37
## 12487 ENSG00000169679  391.34577       4.145891 0.3398087 6.577500e-36
## 9046  ENSG00000148773 1080.00392       4.341723 0.3616491 3.673715e-35
## 11477 ENSG00000165480  121.06319       3.984229 0.3236120 7.013135e-35
## 5110  ENSG00000121152  211.51483       4.233840 0.3543575 6.315500e-34
## 6423  ENSG00000131747 1159.48202       4.176065 0.3611308 8.299477e-33
## 12935 ENSG00000171700  461.50253       2.475897 0.2162880 2.153309e-31
## 5392  ENSG00000123843  129.27485      -3.469274 0.2995853 2.190238e-31
## 3687  ENSG00000109805  334.73959       4.094134 0.3615999 2.353862e-31
## 11271 ENSG00000164611  496.61379       3.671111 0.3229150 4.220011e-31
## 4009  ENSG00000112242  575.60515       2.399186 0.2111830 4.720339e-31
## 1844  ENSG00000089685  598.18916       4.314461 0.3844641 5.260845e-31
## 10002 ENSG00000157456  350.27085       3.846288 0.3409431 7.250601e-31
## 8275  ENSG00000143228  232.28054       3.839719 0.3421960 1.470819e-30
## 4081  ENSG00000112742  275.04632       4.183609 0.3760899 2.478286e-30
## 1372  ENSG00000077152  266.52244       3.316052 0.2964858 3.620661e-30
## 1197  ENSG00000072571  203.57910       3.767188 0.3388903 3.821498e-30
## 2420  ENSG00000101057  791.63169       4.677631 0.4245496 5.013545e-30
## 2516  ENSG00000101440   25.18471      -4.738041 0.4179163 5.225295e-30
## 4351  ENSG00000115163  159.78382       4.494506 0.4012504 9.625444e-30
## 923   ENSG00000065328  144.22937       3.772094 0.3401611 1.043422e-29
## 2523  ENSG00000101447  334.37476       4.109514 0.3763048 1.208282e-29
## 441   ENSG00000024526  225.58850       4.359077 0.3985200 2.076110e-29
## 14873 ENSG00000183287  422.64699      -3.630607 0.3253830 2.888063e-29
## 16917 ENSG00000198759 1127.80522       4.974515 0.4605761 3.667403e-29
## 14703 ENSG00000182481 2184.86118       2.733599 0.2508030 4.656406e-29
## 14757 ENSG00000182749  560.73077      -2.090683 0.1887148 6.188864e-29
## 3837  ENSG00000111206  780.85397       3.453181 0.3189707 6.999166e-29
## 16001 ENSG00000188730  126.42062      -5.363570 0.4823545 9.363982e-29
## 5377  ENSG00000123610  152.47400       5.081451 0.4642499 1.048219e-28
## 1198  ENSG00000072609  646.72286       1.419510 0.1293494 1.514072e-28
## 3846  ENSG00000111247  244.58649       3.034891 0.2809467 1.542488e-28
## 12834 ENSG00000171241  192.96019       3.138808 0.2910370 1.771562e-28
## 8938  ENSG00000147852 1134.30223      -3.043523 0.2772773 1.792106e-28
## 15017 ENSG00000183856  519.38832       4.345821 0.4069458 2.576997e-28
## 8223  ENSG00000142945  495.98642       3.918577 0.3673788 2.604511e-28
## 3141  ENSG00000105664 1740.27329       7.259892 0.6897367 2.897477e-28
## 18234 ENSG00000221955  679.22724       3.435748 0.3214557 3.241857e-28
## 12974 ENSG00000171848  565.55775       3.045456 0.2841631 3.339219e-28
## 9944  ENSG00000156970  295.96475       3.459288 0.3248491 4.514570e-28
## 5805  ENSG00000126787  267.17163       4.092292 0.3856053 9.048349e-28
## 8107  ENSG00000141905 1564.72016      -1.795886 0.1662445 1.107383e-27
##               padj   symbol
## 2280  9.083229e-46     ST13
## 9682  9.083229e-46   MAGED4
## 2216  1.574592e-37  POLDIP3
## 7803  4.537684e-37   DIAPH3
## 1090  1.645585e-36     GNB5
## 16378 2.669069e-34      ADA
## 1770  3.150091e-34     TPX2
## 7554  6.844966e-34    CEP55
## 18107 1.468796e-33     <NA>
## 12487 1.262617e-32     BUB1
## 9046  6.410967e-32    MKI67
## 11477 1.121868e-31     SKA3
## 5110  9.325564e-31    NCAPH
## 6423  1.137977e-29    TOP2A
## 12935 2.627739e-28    RGS19
## 5392  2.627739e-28    C4BPB
## 3687  2.657926e-28    NCAPG
## 11271 4.500407e-28    PTTG1
## 4009  4.769033e-28     E2F3
## 1844  5.049359e-28    BIRC5
## 10002 6.627740e-28    CCNB2
## 8275  1.283356e-27     NUF2
## 4081  2.068399e-27      TTK
## 1372  2.895925e-27    UBE2T
## 1197  2.934299e-27     HMMR
## 2420  3.701539e-27    MYBL2
## 2516  3.714991e-27     ASIP
## 4351  6.598929e-27    CENPA
## 923   6.906735e-27    MCM10
## 2523  7.731394e-27   FAM83D
## 441   1.285581e-26   DEPDC1
## 14873 1.732477e-26    CCBE1
## 16917 2.133317e-26    EGFL6
## 14703 2.628952e-26    KPNA2
## 14757 3.394327e-26    PAQR7
## 3837  3.732111e-26    FOXM1
## 16001 4.858135e-26     VWC2
## 5377  5.295163e-26  TNFAIP6
## 1198  7.402399e-26     CHFR
## 3846  7.402399e-26 RAD51AP1
## 12834 8.190776e-26   SHCBP1
## 8938  8.190776e-26    VLDLR
## 15017 1.136277e-25   IQGAP3
## 8223  1.136277e-25    KIF2C
## 3141  1.235999e-25     COMP
## 18234 1.352841e-25  SLC12A8
## 12974 1.363822e-25     RRM2
## 9944  1.805452e-25    BUB1B
## 5805  3.544737e-25   DLGAP5
## 8107  4.251464e-25     NFIC
final_data_Ascites <- read.csv('Final_Data_LFC_Ascites.csv')

final_data_Ascites$symbol <- mapIds(org.Hs.eg.db, keys = final_data_Ascites$X, column = 'SYMBOL', keytype = 'ENSEMBL')
## 'select()' returned 1:many mapping between keys and columns
final_data_Ascites <- final_data_Ascites[order(final_data_Ascites$pvalue),]

head(final_data_Ascites, 50)
##                     X    baseMean log2FoldChange     lfcSE       pvalue
## 4410  ENSG00000115461 16817.08019      -6.766741 0.4209936 4.156538e-58
## 2280  ENSG00000100380  5724.96499      -2.749551 0.1781409 2.862340e-54
## 1209  ENSG00000072840   769.00105      -3.363033 0.2232460 7.753422e-52
## 2216  ENSG00000100227  1539.32309      -2.416056 0.1613224 2.936158e-51
## 4194  ENSG00000113658  1041.52626      -2.286800 0.1585170 1.053932e-47
## 4809  ENSG00000118640  2766.78334       3.871327 0.2781654 2.250980e-45
## 11261 ENSG00000164574  1551.57933      -3.680226 0.2639437 5.407182e-45
## 1924  ENSG00000091436  1251.49413      -3.798562 0.2736679 2.292355e-44
## 14389 ENSG00000180357   819.64498      -2.095509 0.1508520 3.046458e-44
## 1090  ENSG00000069966   566.39767      -2.530313 0.1842996 2.728464e-43
## 8092  ENSG00000141720  1522.08596      -2.120037 0.1546516 3.372399e-43
## 3841  ENSG00000111229  3753.62933       1.663285 0.1206307 4.872496e-43
## 7918  ENSG00000140543   138.51741      -2.592467 0.1912299 1.532077e-42
## 11027 ENSG00000163820   798.28237      -2.786760 0.2085839 1.632047e-41
## 15073 ENSG00000184207   578.75359       2.887580 0.2170007 1.727614e-41
## 9682  ENSG00000154545    61.40843      10.796944 3.6045524 4.601302e-41
## 1809  ENSG00000089057  1005.89933      -2.735223 0.2068275 6.323177e-41
## 10094 ENSG00000158258  1125.91097      -6.095347 0.4558747 7.334541e-41
## 16378 ENSG00000196839   229.11553       2.746156 0.2083082 1.988212e-40
## 9239  ENSG00000150760  1196.20071      -2.184512 0.1657465 5.495730e-40
## 9251  ENSG00000150907  1129.39294      -3.533868 0.2688621 7.581393e-40
## 7926  ENSG00000140577   937.43722      -2.875797 0.2199557 7.790204e-40
## 11271 ENSG00000164611   496.61379       4.635777 0.3564130 9.545710e-40
## 11873 ENSG00000167074   615.93585      -3.346519 0.2564730 1.743841e-39
## 12704 ENSG00000170653   777.55511      -1.810626 0.1394129 5.215307e-39
## 1558  ENSG00000082175   367.99463      -7.875171 0.6011838 5.722751e-39
## 8489  ENSG00000144445   421.28187      -2.167882 0.1672635 8.997096e-39
## 10100 ENSG00000158301   326.81998      -4.133615 0.3200302 9.061444e-39
## 2252  ENSG00000100320  2020.23680      -2.638264 0.2043416 9.176720e-39
## 7145  ENSG00000136111   743.08883      -4.240211 0.3274465 1.049670e-38
## 4417  ENSG00000115504   825.88386      -2.683715 0.2074528 1.110653e-38
## 18966 ENSG00000249242   299.88110      -4.701757 0.3636611 1.612567e-38
## 6320  ENSG00000131018  1294.67531      -4.585675 0.3573770 1.723938e-38
## 11795 ENSG00000166783  1382.42731      -1.865451 0.1443335 2.355278e-38
## 2831  ENSG00000103657  1028.96224      -2.041921 0.1596197 5.691320e-38
## 6335  ENSG00000131089   432.50559      -2.431705 0.1912510 1.544106e-37
## 2254  ENSG00000100324   462.88611      -1.932270 0.1523835 1.560118e-37
## 13467 ENSG00000174485   273.99563      -2.303004 0.1815890 2.447879e-37
## 15364 ENSG00000185551  3099.39881      -4.350840 0.3417257 3.205223e-37
## 7644  ENSG00000138688  1228.68677      -2.051523 0.1633682 1.434492e-36
## 399   ENSG00000020181  1145.56744      -5.417010 0.4341985 2.411756e-36
## 15709 ENSG00000187240   368.43998      -2.941907 0.2365892 4.054844e-36
## 863   ENSG00000063587   889.69004      -3.179799 0.2559716 5.350762e-36
## 5890  ENSG00000127603  4721.59003      -1.954497 0.1570793 6.035839e-36
## 9996  ENSG00000157404   334.79742      -6.776571 0.5421366 6.416311e-36
## 11981 ENSG00000167553  5217.60943       3.045495 0.2476570 6.839267e-36
## 1107  ENSG00000070366   954.70711      -2.255985 0.1818605 8.455774e-36
## 13342 ENSG00000173714   518.81452     -10.453879 0.8596890 1.146955e-35
## 16609 ENSG00000197724   860.07611      -2.069177 0.1667896 1.202119e-35
## 4147  ENSG00000113319   310.26753      -5.227584 0.4236730 1.268900e-35
##               padj   symbol
## 4410  8.301023e-54   IGFBP5
## 2280  2.858189e-50     ST13
## 1209  5.161453e-48      EVC
## 2216  1.465950e-47  POLDIP3
## 4194  4.209616e-44    SMAD5
## 4809  7.492388e-42    VAMP8
## 11261 1.542669e-41  GALNT10
## 1924  5.722577e-41  MAP3K20
## 14389 6.760089e-41   ZNF609
## 1090  5.449016e-40     GNB5
## 8092  6.122744e-40     <NA>
## 3841  8.109052e-40    ARPC3
## 7918  2.353624e-39     DET1
## 11027 2.300146e-38    FYCO1
## 15073 2.300146e-38      PGP
## 9682  5.743288e-38   MAGED4
## 1809  7.428246e-38  SLC23A2
## 10094 8.137673e-38   CLSTN2
## 16378 2.089820e-37      ADA
## 9239  5.487761e-37    DOCK1
## 9251  7.071735e-37    FOXO1
## 7926  7.071735e-37    CRTC3
## 11271 8.288582e-37    PTTG1
## 11873 1.451094e-36      TEF
## 12704 4.166196e-36     ATF7
## 1558  4.395733e-36      PGR
## 8489  6.319596e-36  KANSL1L
## 10100 6.319596e-36  GPRASP2
## 2252  6.319596e-36   RBFOX2
## 7145  6.987653e-36   TBC1D4
## 4417  7.155111e-36    EHBP1
## 18966 1.006393e-35 TMEM150C
## 6320  1.043296e-35    SYNE1
## 11795 1.383449e-35    MARF1
## 2831  3.247467e-35    HERC1
## 6335  8.420842e-35  ARHGEF9
## 2254  8.420842e-35     TAB1
## 13467 1.286490e-34  DENND4A
## 15364 1.641321e-34    NR2F2
## 7644  7.162062e-34    BLTP1
## 399   1.174761e-33   ADGRA2
## 15709 1.928078e-33  DYNC2H1
## 863   2.485118e-33   ZNF275
## 5890  2.739585e-33    MACF1
## 9996  2.847559e-33      KIT
## 11981 2.969283e-33   TUBA1C
## 1107  3.592984e-33     SMG6
## 13342 4.772049e-33  WFIKKN2
## 16609 4.899493e-33     PHF2
## 4147  5.068241e-33  RASGRF2